Provenance, Anonymisation and Data Environments: a Unifying Construction
This work addresses incremental improvements in data anonymization for organizations handling sensitive information, focusing on operationalizing risk management through provenance modeling.
The paper tackles the challenge of modeling data environments within the Anonymisation Decision-making Framework (ADF) to enhance risk management in data exchanges, by proposing and analyzing four implementations using W3C PROV to integrate provenance information.
The Anonymisation Decision-making Framework (ADF) operationalizes the risk management of data exchange between organizations, referred to as "data environments". The second edition of ADF has increased its emphasis on modeling data flows, highlighting a potential new use of provenance information to support anonymisation decision-making. In this paper, we provide a use case that showcases this functionality more. Based on this use case, we identify how provenance information could be utilized within the ADF framework, and identify a currently un-met requirement which is the modeling of \textit{data environments}. We show how data environments can be implemented within the W3C PROV in four different ways. We analyze the costs and benefits of each approach, and consider another use case as a partial check for completeness. We then summarize our findings and suggest ways forward.